Scalable and Efficient Associative Processor Solution to Guarantee Real-Time Requirements for Air Traffic Control Systems

https://kent-islandora.s3.us-east-2.amazonaws.com/node/17407/87333-thumbnail.jpgThis paper proposes a solution to air traffic control (ATC) using an enhanced SIMD machine model called an Associative Processor (AP). Our solution differs from previous ATC systems that are designed for MIMD computers and have a great deal of difficulty meeting the predictability requirements for ATC, which are critical for meeting the strict certification standards required for safety critical software components. The proposed AP solution supports accurate predictions of worst case execution times and guarantees all deadlines are met. Furthermore, the software developed based on the AP model is much simpler and smaller in size than the current corresponding ATC software. As the associative processor is built from SIMD hardware, it is considerably cheaper and simpler than the MIMD hardware currently used to support ATC. We have designed a prototype for eight ATC real-time tasks on ClearSpeed CSX600 accelerator that is used to emulate AP. Performance is evaluated in terms of execution time and predictability and is compared to the fastest host-only version implemented using OpenMP on an 8core multiprocessor (MIMD). Our extensive experiments show that the AP implementation meets all deadlines that can be statically scheduled. To the contrary, some tasks miss their deadlines when implemented on MIMD. It is shown that the proposed AP solution will support accurate and meaningful predictions of worst case execution times and will guarantee that all deadlines are met.</p

On the power of the Multiple Associative Computing (MASC) Model related to that of reconfigurable bus-based models

Abstract: The MASC model is a multi-SIMD model that uses control parallelism to coordinate the interaction of data parallel threads. It supports a generalized associative style of parallel computation. The power of this model has been compared to that of priority CRCW PRAM and enhanced meshes. In this paper, we present the work on simulations between MASC and reconfigurable bus-based models, in particular, different versions of the Reconfigurable Multiple Bus Machine (RMBM). It is shown that MASC and the Basic RMBM (B-RMBM) can simulate each other in constant time if the number of buses on the B-RMBM is �(j) where j is the number of MASC instruction streams. Thus, when these two models satisfy the preceding condition, they have the same power. Simulations of other stronger versions of RMBM using MASC are also considered. Since the RMBM model has been shown to be as powerful as a general Reconfigurable Mesh (RM), our simulations can be used to establish a relationship between MASC and RM. As RM has been widely accepted as an extremely powerful model, our work gives a better understanding of the MASC model and provides useful information concerning its power. Key Words: parallel computational model, associative computing, simulation, reconfigurable buses, Multi-SIMD 1

A software implementation of a cycle precision simulator of a multiple associative model

The Multiple Associative Computing (MASC) parallel model is a generalization model of an Associative Computing (ASC) parallel model designed to support multiple ASC data parallel threads by using control parallelism. The MASC model is designed to combine the advantages of both Single Instruction Stream Multiple Data Streams (SIMD) and Multiple Instruction Streams Multiple Data Streams (MIMD) models. Here is the first time that a complete description of MASC model has been implemented (in software) true to its original description. A cycle precision simulator is built to demonstrate the performance of MASC on various multithreaded algorithms. The simulator is a software prototype for the model with sufficient software details to allow it to be converted into a hardware prototype of the model. If a reasonable limit for the number of threads simultaneously supported is assumed, the resulting hardware design is not only easily to implement, but can easily support a huge number of processing units and is a excellent candidate architecture for supporting large scale (e.g., terascale and petascale) computing. Experimental results shows that, when processing large-scale instances using multiple workers, the algorithm executed by the MASC model using a static task assignment scheme provides strong scaling with constant time overhead.</p

A Multiple Associative Model to Support Branches in Data Parallel Applications Using the Manager-Worker Paradigm

https://kent-islandora.s3.us-east-2.amazonaws.com/node/16809/87018-thumbnail.jpgASC (associative computing model) and MASC (multiple associative computing model) have long been studied in the Department of Computer Science at Kent State University. While the previous studies provide the background and the basic definition of the model, the description of the interactions between the instruction streams (ISs) is very brief, high level, and incomplete. One change here is that we specify the interaction between ISs and consider that all of the ISs operate on the same clock in order to support predictable worst case computation times, while earlier the ISs were assumed to interact in a MIMD type fashion. This paper provides a detailed explanation as to how these interactions can be supported in the case where only a few ISs are supported.</p

Simulation between Enhanced Meshes and the Multiple Associative Computing (MASC) Model

MASC (for Multiple Associative Computing) is a practical, highly scalable joint control parallel, data parallel model that naturally supports massive parallelism and a wide range of applications. In this paper, we propose efficient algorithms for the MASC model with a 2-D mesh to simulate enhanced meshes, e.g., meshes with multiple broadcasting (MMB), and basic reconfigurable meshes (BRM). The results not only show the power of the MASC model in terms of the enhanced mesh models but also provide an automatic conversion of numerous algorithms designed for enhanced meshes to the MASC model

Simulation of Enhanced Meshes with MASC, a MSIMD Model

Abstract: MASC (for Multiple Associative Computing) is a joint control parallel, data parallel model that provides a practical, highly scalable model that naturally supports small to massive parallelism and a wide range of applications. In this paper, we present efficient algorithms for a MASC model with a 2-D mesh to simulate enhanced meshes. Let MASC(n, j) denote a MASC model with n processing elements and j instruction streams. It is shown that a MASC(n, j) model with a 2-D mesh is strictly more powerful than a n × n MMB (Mesh with Multiple Broadcasting) when j =Ω ( n). Simulation of a n × n MMB by MASC(n, j) with a 2-D mesh runs in O(1) time and requires no extra memory. Simulating a n × n BRM (Basic Reconfigurable Mesh) with MASC(n, j) with a 2-D mesh takes O ( n) extra time with O(n) extra memory when j = Ω ( n). The reverse simulations of MMB or BRM with MASC with a 2-D mesh is also given. These simulations not only provide information about the power of the MASC model and also provide an automatic conversion of numerous algorithms designed for enhanced meshes to the MASC model. Key Words: parallel models of computation, associative computing, simulation, mesh with multiple broadcasting, enhanced meshes, MSIMD 1

Solving a 2D Knapsack Problem on an Associative Computer Augmented with a Linear Network

This paper describes a parallelization of the sequential dynamic programming method for solving a 2D knapsack problem where multiples of n rectangular objects are optimally packed into a knapsack of size L # W and are only obtainable with guillotine-type #side to side# cuts. The parallel algorithm is described and analyzed for the associative model. The associative model #ASC# for parallel computation supports a generalized version of an associative style of computing that has been used since the introduction of associative SIMD computers in the early 1970&apos;s. In particular, this model supports data parallelism, constant time maximum and minimum operations, one or more instruction streams #ISs# which are sent to an equal number of partition sets of processors, and assignment of tasks to the ISs using control parallelism. This algorithm runs in O#W #n+L+W ## time using O#L# processors, where L # W for a 2D knapsack problem with a capacityofL#W . This result is cost optimal with respect to the best sequential implementation. Moreover, an e#cient ASC algorithm for this well-known problem should give insighttohow the associative model compares to other parallel models

Simulating PRAM with a MSIMD model (ASC)

The ASC (MSIMD) model for parallel computation supports a generalized version of an associative style of computing that has been used since the introduction of associative SIMD computers in the early 1970\u27s. In particular, this model supports data parallelism, constant time maximum and minimum operations, one or more instruction streams (ISs) which are sent to a unique set in a dynamic partition of the processors, and assignment of tasks to the ISs using control parallelism. ASC also allows a network to interconnect the processing elements (PEs). This paper shows how ASC can simulate synchronous PRAM, and the converse. These results provide an important step in defining the power of associative model in terms of PRAM which is the most studied parallel model. Also, these simulations will provide numerous algorithms for ASC by giving an automatic method of converting algorithms from PRAM to ASC.</p